Abstract: In recent years numerous attempts to understand the human brain were
undertaken from a network point of view. A network framework takes into account
the relationships between the different parts of the system and enables to
examine how global and complex functions might emerge from network topology.
Previous work revealed that the human brain features 'small world'
characteristics and that cortical hubs tend to interconnect among themselves.
However, in order to fully understand the topological structure of hubs one
needs to go beyond the properties of a specific hub and examine the various
structural layers of the network. To address this topic further, we applied an
analysis known in statistical physics and network theory as k-shell
decomposition analysis. The analysis was applied on a human cortical network,
derived from MRI\DSI data of six participants. Such analysis enables us to
portray a detailed account of cortical connectivity focusing on different
neighborhoods of interconnected layers across the cortex. Our findings reveal
that the human cortex is highly connected and efficient, and unlike the
internet network contains no isolated nodes. The cortical network is comprised
of a nucleus alongside shells of increasing connectivity that formed one
connected giant component. All these components were further categorized into
three hierarchies in accordance with their connectivity profile, with each
hierarchy reflecting different functional roles. Such a model may explain an
efficient flow of information from the lowest hierarchy to the highest one,
with each step enabling increased data integration. At the top, the highest
hierarchy (the nucleus) serves as a global interconnected collective and
demonstrates high correlation with consciousness related regions, suggesting
that the nucleus might serve as a platform for consciousness to emerge.